{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "0b4285e3",
            "metadata": {},
            "source": [
                "Copyright (c) MONAI Consortium  \n",
                "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
                "you may not use this file except in compliance with the License.  \n",
                "You may obtain a copy of the License at  \n",
                "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
                "Unless required by applicable law or agreed to in writing, software  \n",
                "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
                "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
                "See the License for the specific language governing permissions and  \n",
                "limitations under the License.\n",
                "\n",
                "# Vector Quantized Variational Autoencoders for 3D reconstruction of images\n",
                "\n",
                "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] on 3D images.\n",
                "\n",
                "Here, we will train our VQVAE model to be able to reconstruct the input images.  We will work with the Decathlon Dataset available on [MONAI](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset). In order to train faster, we will select just one of the available tasks (\"Task01_BrainTumour\").\n",
                "\n",
                "The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial.\n",
                "\n",
                "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n",
                "\n",
                "\n",
                "### Setup environment"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "2859b87c",
            "metadata": {},
            "outputs": [],
            "source": [
                "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n",
                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
                "%matplotlib inline"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "e6a4ca0e",
            "metadata": {},
            "source": [
                "### Setup imports"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "bb14df03",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "MONAI version: 1.4.0rc6\n",
                        "Numpy version: 1.26.4\n",
                        "Pytorch version: 2.3.1+cu121\n",
                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
                        "MONAI rev id: 6a0e1b043ba2890e1463fa49df76f66e56a68b08\n",
                        "MONAI __file__: /home/<username>/miniconda3/envs/monai/lib/python3.11/site-packages/monai/__init__.py\n",
                        "\n",
                        "Optional dependencies:\n",
                        "Pytorch Ignite version: 0.4.11\n",
                        "ITK version: 5.4.0\n",
                        "Nibabel version: 5.2.1\n",
                        "scikit-image version: 0.23.2\n",
                        "scipy version: 1.13.1\n",
                        "Pillow version: 10.3.0\n",
                        "Tensorboard version: 2.17.0\n",
                        "gdown version: 5.2.0\n",
                        "TorchVision version: 0.18.1+cu121\n",
                        "tqdm version: 4.66.4\n",
                        "lmdb version: 1.4.1\n",
                        "psutil version: 5.9.0\n",
                        "pandas version: 2.2.2\n",
                        "einops version: 0.8.0\n",
                        "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
                        "mlflow version: 2.14.0\n",
                        "pynrrd version: 1.0.0\n",
                        "clearml version: 1.16.2rc0\n",
                        "\n",
                        "For details about installing the optional dependencies, please visit:\n",
                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "import os\n",
                "import shutil\n",
                "import tempfile\n",
                "import time\n",
                "\n",
                "import matplotlib.pyplot as plt\n",
                "import numpy as np\n",
                "from tqdm import tqdm\n",
                "import torch\n",
                "from torch.nn import L1Loss\n",
                "\n",
                "from monai import transforms\n",
                "from monai.apps import DecathlonDataset\n",
                "from monai.config import print_config\n",
                "from monai.data import DataLoader\n",
                "from monai.utils import set_determinism, ensure_tuple\n",
                "from monai.networks.nets import VQVAE\n",
                "\n",
                "print_config()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "352bd8ea",
            "metadata": {},
            "outputs": [],
            "source": [
                "# for reproducibility purposes set a seed\n",
                "set_determinism(42)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "d0618c17",
            "metadata": {},
            "source": [
                "### Setup a data directory\n",
                "\n",
                "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not\n",
                "specified a temporary directory will be used."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "4fc6c2f9",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "../data\n"
                    ]
                }
            ],
            "source": [
                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
                "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
                "print(root_dir)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "a342ff79",
            "metadata": {},
            "source": [
                "### Setup used transforms and download dataset"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "1e1b3bd0",
            "metadata": {},
            "outputs": [],
            "source": [
                "train_transform = transforms.Compose(\n",
                "    [\n",
                "        transforms.LoadImaged(keys=[\"image\"]),\n",
                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
                "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
                "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
                "    ]\n",
                ")\n",
                "\n",
                "val_transform = transforms.Compose(\n",
                "    [\n",
                "        transforms.LoadImaged(keys=[\"image\"]),\n",
                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
                "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
                "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
                "    ]\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "399ab576",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "2024-09-05 22:11:47,598 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
                        "2024-09-05 22:11:47,600 - INFO - File exists: ../data/Task01_BrainTumour.tar, skipped downloading.\n",
                        "2024-09-05 22:11:47,602 - INFO - Non-empty folder exists in ../data/Task01_BrainTumour, skipped extracting.\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "Loading dataset: 100%|██████████| 388/388 [04:57<00:00,  1.30it/s]\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "2024-09-05 22:16:58,676 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
                        "2024-09-05 22:16:58,678 - INFO - File exists: ../data/Task01_BrainTumour.tar, skipped downloading.\n",
                        "2024-09-05 22:16:58,679 - INFO - Non-empty folder exists in ../data/Task01_BrainTumour, skipped extracting.\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "Loading dataset: 100%|██████████| 96/96 [01:14<00:00,  1.29it/s]\n"
                    ]
                }
            ],
            "source": [
                "batch_size = 16\n",
                "num_workers = 8\n",
                "\n",
                "train_ds = DecathlonDataset(\n",
                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=train_transform, section=\"training\", download=True\n",
                ")\n",
                "\n",
                "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
                "\n",
                "val_ds = DecathlonDataset(\n",
                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n",
                ")\n",
                "\n",
                "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, persistent_workers=True)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "7c896e0a",
            "metadata": {},
            "source": [
                "### Visualize the training images"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "5a32be9f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x600 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.subplots(1, 4, figsize=(10, 6))\n",
                "for i in range(4):\n",
                "    plt.subplot(1, 4, i + 1)\n",
                "    plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
                "    plt.axis(\"off\")\n",
                "plt.tight_layout()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "5ae4f2c7",
            "metadata": {},
            "source": [
                "### Define network, optimizer and losses"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "b28d46a4",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Using cuda:2\n"
                    ]
                }
            ],
            "source": [
                "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
                "print(f\"Using {device}\")\n",
                "model = VQVAE(\n",
                "    spatial_dims=3,\n",
                "    in_channels=1,\n",
                "    out_channels=1,\n",
                "    channels=(256, 256),\n",
                "    num_res_channels=256,\n",
                "    num_res_layers=2,\n",
                "    downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),\n",
                "    upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),\n",
                "    num_embeddings=256,\n",
                "    embedding_dim=32,\n",
                ").to(device)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "dbefd7a9",
            "metadata": {},
            "outputs": [],
            "source": [
                "optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-4)\n",
                "l1_loss = L1Loss()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8fe3cb3c",
            "metadata": {},
            "source": [
                "### Model training\n",
                "Here, we are training our model for 100 epochs (training time: ~60 minutes)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "7ba11fab",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "Epoch 0: 100%|██████████████████| 25/25 [00:07<00:00,  3.55it/s, recons_loss=0.106, quantization_loss=1.79e-6]\n",
                        "Epoch 1: 100%|█████████████████| 25/25 [00:06<00:00,  3.97it/s, recons_loss=0.0889, quantization_loss=5.69e-5]\n",
                        "Epoch 2: 100%|█████████████████| 25/25 [00:06<00:00,  3.96it/s, recons_loss=0.0647, quantization_loss=1.46e-5]\n",
                        "Epoch 3: 100%|█████████████████| 25/25 [00:06<00:00,  3.76it/s, recons_loss=0.0416, quantization_loss=7.17e-6]\n",
                        "...\n",
                        "Epoch 98: 100%|███████████████| 25/25 [00:06<00:00,  3.95it/s, recons_loss=0.00858, quantization_loss=1.74e-6]\n",
                        "Epoch 99: 100%|████████████████| 25/25 [00:06<00:00,  3.96it/s, recons_loss=0.0085, quantization_loss=2.13e-6]\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "train completed, total time: 653.6743619441986.\n"
                    ]
                }
            ],
            "source": [
                "max_epochs = 100\n",
                "val_interval = 10\n",
                "epoch_recon_loss_list = []\n",
                "epoch_quant_loss_list = []\n",
                "val_recon_epoch_loss_list = []\n",
                "intermediary_images = []\n",
                "n_example_images = 4\n",
                "\n",
                "total_start = time.time()\n",
                "for epoch in range(max_epochs):\n",
                "    model.train()\n",
                "    epoch_loss = 0\n",
                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n",
                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
                "    for step, batch in progress_bar:\n",
                "        images = batch[\"image\"].to(device)\n",
                "        optimizer.zero_grad(set_to_none=True)\n",
                "\n",
                "        # model outputs reconstruction and the quantization error\n",
                "        reconstruction, quantization_loss = model(images=images)\n",
                "\n",
                "        recons_loss = l1_loss(reconstruction.float(), images.float())\n",
                "\n",
                "        loss = recons_loss + quantization_loss\n",
                "\n",
                "        loss.backward()\n",
                "        optimizer.step()\n",
                "\n",
                "        epoch_loss += recons_loss.item()\n",
                "\n",
                "        progress_bar.set_postfix(\n",
                "            {\"recons_loss\": epoch_loss / (step + 1), \"quantization_loss\": quantization_loss.item() / (step + 1)}\n",
                "        )\n",
                "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
                "    epoch_quant_loss_list.append(quantization_loss.item() / (step + 1))\n",
                "\n",
                "    if (epoch + 1) % val_interval == 0:\n",
                "        model.eval()\n",
                "        val_loss = 0\n",
                "        with torch.no_grad():\n",
                "            for val_step, batch in enumerate(val_loader, start=1):\n",
                "                images = batch[\"image\"].to(device)\n",
                "\n",
                "                reconstruction, quantization_loss = model(images=images)\n",
                "\n",
                "                # get the first sample from the first validation batch for\n",
                "                # visualizing how the training evolves\n",
                "                if val_step == 1:\n",
                "                    intermediary_images.append(reconstruction[:n_example_images, 0])\n",
                "\n",
                "                recons_loss = l1_loss(reconstruction.float(), images.float())\n",
                "\n",
                "                val_loss += recons_loss.item()\n",
                "\n",
                "        val_loss /= val_step\n",
                "        val_recon_epoch_loss_list.append(val_loss)\n",
                "\n",
                "total_time = time.time() - total_start\n",
                "print(f\"train completed, total time: {total_time}.\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "5fc01098",
            "metadata": {},
            "source": [
                "### Learning curves"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "ac5f7c56",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.style.use(\"ggplot\")\n",
                "plt.title(\"Learning Curves\", fontsize=20)\n",
                "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_recon_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
                "plt.plot(\n",
                "    np.linspace(val_interval, max_epochs, int(max_epochs / val_interval)),\n",
                "    val_recon_epoch_loss_list,\n",
                "    color=\"C1\",\n",
                "    linewidth=2.0,\n",
                "    label=\"Validation\",\n",
                ")\n",
                "plt.yticks(fontsize=12)\n",
                "plt.xticks(fontsize=12)\n",
                "plt.xlabel(\"Epochs\", fontsize=16)\n",
                "plt.ylabel(\"Loss\", fontsize=16)\n",
                "plt.legend(prop={\"size\": 14})\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6df78259",
            "metadata": {},
            "source": [
                "###  Plotting  evolution of reconstructed images"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "040b52ba",
            "metadata": {
                "lines_to_next_cell": 2
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1850x3050 with 10 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Plot every evaluation as a new line and example as columns\n",
                "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n",
                "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n",
                "fig.set_size_inches(18.5, 30.5)\n",
                "ax = ensure_tuple(ax)\n",
                "\n",
                "for image_n in range(len(val_samples)):\n",
                "    reconstructions = intermediary_images[image_n]\n",
                "    reconstructions = np.concatenate(\n",
                "        [\n",
                "            reconstructions[0, :, :, 15],\n",
                "            np.flipud(reconstructions[0, :, 24, :].T),\n",
                "            np.flipud(reconstructions[0, 15, :, :].T),\n",
                "        ],\n",
                "        axis=1,\n",
                "    )\n",
                "\n",
                "    ax[image_n].imshow(reconstructions, cmap=\"gray\")\n",
                "    ax[image_n].set_xticks([])\n",
                "    ax[image_n].set_yticks([])\n",
                "    ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1c3b5cff",
            "metadata": {},
            "source": [
                "### Plotting the reconstructions from final trained model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "709f9c57",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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+qFKp5DLIcrkc/P2lL30pAODuu+8Obq847bTTABRf0yThkUceAQBcdtllI79518EK4nvuucdVe7xrXOl9jNieeMELXoA3v/nNePzxx/GHf/iH6fKVvj9c/4tf/GJQkSToX0LD595xxx0Alm37UFGtVnH66afjHe94B/7X//pfAA527SWYZl1t1JvnIx955JERpbXdbuMnfuIn8MQTT+Cb3/xm4f5Xc35593jv3r24//770Ww2MzVO2wWRDAhuv/32kXEIPvjBD+I73/kOzj333Ey9APNkf/Inf5JZ/7bbbksNy+KII47Av/7rv7r96/m7ZzjAwaK/Wq2G//Jf/gseeuihkd97vV7GQZ155pk46aST8Dd/8ze46aab3GuaNGj/ZMU3v/lNvPe97x1Z/1nPehbOOeccPPLII/jIRz6S+e0LX/jCSL0AAFx88cU44YQT8KEPfQi33HKLex733HMPFhYWVncREVsGv/3bv41Go4E/+IM/SHPCK7XD008/HWeeeSbuv/9+vO997xtZ/8knn0S32wVwcGyDk046CV/+8pfxiU98IrPeJz7xCdx999048cQTcdZZZ636mu677z433UkVjQoIcNAfAcA//dM/repYJ598MmZnZ3HTTTfhRz/6Ubq80+kE0xFc/ta3vnXkPIfDYRq5r/b83vjGN6JWq+EDH/hAGnwQv/M7v4P9+/fjjW98Y9oVczshpgkEF154IS655BJccsklaZ/8z3/+89i9ezc+/OEPZ9a98sorcd111+G9730vvvWtb+GUU07BQw89lI5T8MlPfnJk/6985Stx77334tWvfjXOPvtsNBoNvPCFL8SFF16Y/v6Xf/mXuPDCC3HaaaehVqvh7LPPxtlnn42TTz4Zf/Znf4Zf+qVfwvOf/3y8+tWvxoknnoilpSX80z/9E+6++24ceeSReOCBBwAsF0C96lWvwmWXXZYZZ4ADbnzhC19Y/5u6Alx++eW47rrr8Ou//uu444478NznPhcPP/wwPvvZz+LSSy/FjTfeOLLNhz70Ibz85S/Hf/7P/xm33HJLOs7AJz/5SVx88cW46aabMoVbtVoNn/rUp3DBBRfgZ3/2Z3HmmWfi1FNPRavVwj//8z/j3nvvxXe/+1384Ac/yDjGiO2HY445BldddRXe//734/d///fx3ve+d8V2CBysYD/nnHPwzne+E5/85CdxzjnnIEkSPPzww/jf//t/44EHHsDxxx+PUqmEP//zP8erXvUqvPa1r8XFF1+Mk08+GQ8++CA+85nPYGZmBv/zf/7PwkLEPNxwww34yEc+grPOOgsnnHACdu3ahe985zu4+eab0Wg0MmOgvOxlL0Or1cL111+PJ598Mq0N+NVf/dWxZPRarYa3v/3t+G//7b/hRS96ES655BL0+3186UtfwtFHH50WGCv+03/6T7j77rtxww034LnPfS4uvvhiHHnkkfj+97+P22+/Hb/0S7+UphBXc37HH388rr/+evzKr/wKTjvtNPyH//AfcOSRR+Kuu+7CPffcg5NPPtklbdsCG9y18ZCAnHEG3vSmN7nbeOMDaH/Vm2++OXnpS1+atFqtZMeOHcmll16aPPjgg+6+/uEf/iF5zWtek0xPTyftdjt5xStekdx5553B/q9zc3PJVVddlRxzzDFJpVIZOc8nnngief3rX5884xnPSMrlsttH9+///u+TN73pTcmznvWspF6vJ7t27Uqe//znJ295y1uS2267beQcv/71rycXXHBBMj09nUxPTyevfOUrk6985StuH9w8FI0z8Oijjwa38Y4Rukf/+I//mFx44YXJkUcembRareS0005L/uRP/iT3uX77299OLrnkkmTHjh1Jq9VKXvrSlyaf/exnk+uuuy4BkHz6058e2eaJJ55I3vGOdyTPf/7zk6mpqaTdbifPec5zkssuuyy54YYbkqWlpbHuS8TmhudDFD/84Q+TVquVtFqt5Ic//GG6fKV2+OMf/zj5rd/6reTEE09MGo1GsmPHjuSFL3xh8s53vjOZn5/PrPvAAw8kb3zjG5OjjjoqqVaryVFHHZW84Q1vSB544IGR/ebZmGczX/3qV5OrrroqecELXpDs2rUraTabyQknnJBcccUVyf/5P/9nZB+f//znk5e+9KVJu91O7xVtfRwfMhwOk/e+973Js5/97KRWqyXHHnts8pu/+ZvJ/Px87rgrH//4x5Ozzz47mZ2dTRqNRnL88ccnv/iLv5jcd999a3J+X/ziF5NXvepVyc6dO5N6vZ6ccMIJyW/+5m8mTz/99Mi6qxlTZjOilCTbeFqu/4+PfexjuPLKK/HRj340M/pfxObGG97wBvzFX/wFHnjggVWPHhkRERGxHRBrBiI2NYbDoVt9fdttt+HGG2/EKaecEolARERERAFizUDEpkav18Oxxx6Lc889FyeffDKq1Sr+8R//EV/60pdQr9fxoQ99aKNPMSIiImLiEclAxKZGrVbDVVddhdtvvx1f+9rXsLCwgD179uAXfuEXcM0116zbSGYRERERWwmxZiAiIiIiImKbI9YMREREREREbHNEMhAREREREbHNEclARERERETENsfYBYSrnd4yIiJi7bAZS3yi74iI2HgU+Y6oDERERERERASwXchsJAMRERERERHbHHGcgYiIiIiICAdUBUqlkiuzW9VgM6bxiEgGIrYUSqUSSqUSKpVKOt95uVweMdrhcIgkSdI/Bf8fDAaFc9BHRERsftBnAEClUkl9RrVaRblcRpIkGAwGI76iVCqls0iqP+n3+xgOh+nfZiAJkQxEbBmQCJTLZdTrddTr9YxBKwaDAfr9PpIkyRirfvZ6vZHlERERWw+VSiX1E41GA9VqFZVKBfV6PfUd9BcaQJTL5fR3bfwXFxextLSE4XCIXq+HwWCwYdc2LiIZiNiU0Eif32mY5XIZtVoNtVoN5XJ5hAwkSZIyf1UI+Bs/qQqECENERMTmBgOIarWa+gv6DpKBarWK4XCIcrmc8QncnooClQNVA/id23hK5KQgkoGITQdl7LVaLVUAmBool8uYmppCo9FAqVTKsHsaYr/fT5m7hyRJsLi4mLL6breLfr+PwWCQbjccDjcF449Yfyg5VWcfyjVHHB7wuWgkX6lUUCqVUKvV0Gg0Un/RarVS30LfQYWAdm/tXdORg8EgJQSLi4vo9/vo9/vodDrptvQn6kcmBZEMRGw6VCoVNJtNVKvV1Iir1WpquJVKBa1WC81mc4QMAEhzer1eLzVGVReoGHS73VTu279/f/p9YWEhNfTNkg+MWD8wNcWoz9anREKwMdDnQPvXQGJqagrT09Mol8vYsWMHZmZmUK1W0Ww2U5WAygGDB48M1Go1AEh9Av0LG/wDBw6kvmN+fj71PdzXpKgFW44M8OHxj8u8T2/bPKhMPM73iLUDHS4Zfb1eTz8bjUaqCJAQ8JNpg2q1OvKcGCFw//xkJEBDL5VKaDQa6e/8jQ2ASoIRmxP2PfCKTvm7Xa7rhhz7uH5i0qXkzQatI2KkTzWg0WikaQD6EvoO2r0+a8++tdiQz8wGGLVaLf2NRCRJEtRqtbRI2StOPNzYMmSAD67RaKSsjoUgWkSmOWTPsC20OISsTyUeFqINh0MsLS2l38kSgUgMDgVKAFqtFmq1GqamprBz584MIaBUByB93tYR0GC5zPY24HK+I9VqNX3mU1NT6fNlyqDb7aZMv9vtotvtbqrq4YhRkhnyHcBykZlHBghtMLz6E60ypx+hT2HDwKjS1qpErAwaIExNTaFWq6HZbGJ6ejotMCYxaDabaDabmUCCGA6H7rNXf8HjAcu1S1qUyHYi5Ds6nU767DcqqNgSZEDZPBsLvgCMGpX18bt2C7H7shLTYDDA4uJiWmXe7XZTY15cXEx/Z4OghhyNefXgM6rVaqkRT09P44gjjsg8U0r71pC0kVcJlwarv3N9GjWjCJIByn/MBy4sLKBSqaDf76NUKmFpaSklHPGZbw7YHihsEJhm4nKmn0gWCOs/vGIx9QOUiJlX7na7qU/RHLJuH9+l1YG2XKvV0G63U9+xa9eutF2gAsDAwgaI9BmDwSDjI/QYVlW0xYZUBvr9PprNJgaDATqdThpslMvllAB6PuxwYdOSAXXmWjhGI2ZembKMJx97ZED3D/jyMRsnlQdVGhoMBpkGiA/ZGnc08jB4/6j0NBqN9Hlq1x8r9XvkzuZs7Xe7vecMdF/K+ulUqBqQFNLhx2c8eVDfobUmDB6YN7YFqDY6DKURrFys9QS6XP/XhoTvmEaKMbhYGbR7caPRSJ8t/UnI1omitI/CSzF5DbqqCBqULi0tpf4DwIY9501HBnjD6/U6pqamUiNmJSgfOhtsNhZeg6EPh402l3sPmPuqVCpoNBrpQ+ND5EMdDofodDqpYtDtdjN9Tsn+uF1EFiRr9XodRxxxBKanp1Gr1TAzM5OmBfjs9dmGniF/008rv3oO3iNtSghIOofDIVqtFmZnZ9Hv97F3717s27cvI/lGbDy0ipxRf7vdTgtQVSqu1WrBlAAb/LyeKPa7vnesP9Hqc74n9BdUGCkra9GZ7fYaycEyaJ/1eh27d+/GzMwM6vU6du7cmbYL7H1kG28N4vibl64JFYl65NAGgAxgtA2ZmppCu90e8R2He3yCTUcGAGQa5Gq1ina7jdnZ2TRSI6NnowL4RqPEgNIOl3vH1N9to8GGiIbLIhRKyGSAWkFqI9aIg+D9qlaraLVa2LlzZ/qdBM9KempsXB5i914hFzAq+eq69vwYVbKSmA2M5gPp3CMmA/rc6Dv0/dJAgg7bNvo2muf/oQjTYjgcpjUItpaA/2u9CnAwyNBo0xLZ6EOWQT/N5zkzM4NGo4HZ2dmUhBUVbwLLBCE0Uin9tw00i85NRzmk76CvY8q50+mk7Yj6j/VuLzYFGaCDV1m20Wig3W6nBq15PcI2CN7DspKdbuvBY4A2lQAsy0DMB/H8GUlqVOD98Zy8yCJ0zlsBTA0wLcBuPja9Y1MCNlev6YGi6MlGA17hF//XPLCurwWJLFKyIxxuxec1yaA9qhpAu2y1WqkyoL4jr1HPe35Fz1bfI76X6ks0BakScpIkaVDBdbT6XN/RvAG0NGDZquBz5rNW/6GKTugZ629FeXsv0FiNfdN3kCAypaFKkT3OemHiyQAbh0qlgna7jd27d6e5IK8riCfpcj/2RQhJ9eoULFvUffN3zfmpU+HIVTRmz4BVUQCQqS/QghJNLTDVkCdVbkawUZ6ZmUl7C+zcuRPtdjvTC0QbXru9B+sE+S7YbS2hCBWD8VnadFK9XsdwOMTOnTvRbDbTugHthRIJweEB7Y9qUqvVSgvHbDcyLSa2ds3vxEodf8g+lQTwf30v2RV2amoqtX0qi7aWgOeqkaz6Du3hpL5jK72LvH/tdhvtdhuNRgM7d+7Ejh070pSxp+ja56P+3LtHqiB7+9D92+DCLuO2TG+zHSiXy2kvE67D/9cTE08G2MjSgFutVmakKDYOfEB0vLo9MFoIprBSnyc/e9t55wog01jRIMla7THZWKjBatcjNWhuo2NkbzXQcFnApVW+tjtoqPhTYR1fqVQqJFAhNUCjfK5n609KpVKaj9auRVuJtE06VDlilN1sNlPJWEetDNn6OP8XYSXPXIsMleiSFDB9oMGDEkx+tzUIVjXIu97NDlWP1X94KiKwds/bth/jQgtMuW2j0UgJgTenynpiosgAHxqLPCgPT01NpUVj2hdUHbHtF6r7UqftMXtbD8B92Giu6OXQRsOORsVz4v61OIkPXo+p+9Nikl6vB2B5tCtlkJsZ2niy9wDv0WqvTY3NGh6LhYDxin9CpNCrNVFVqNVqpc9pYWEBS0tLq7qWiGLQjikN12q1NFfMfuasOQnBc+yeMuAhr/EvajDse+ZFmEwb2FonPb7WIPCT/qfX66WNzdLSUqaYeTP7Dy34ZDGefc4aDHrXGrp+Jf5WjbV2v9p0gQ0q6vU6AKTpAipC660OTBQZ0L68O3fuxPT0dFogyIetA8xodb/KbWow/ONNVBme+wCyPQtsRK6DB9kHbqvWtYeBlelsF0cqG17/Uqsu6AAVLEbUQW42u0HzfjCK01EF1SAt6VOD1Ou391JVGQApGciLBkJSMfepUZeqFnRE5XIZs7OzqNVqaY+SSAbWB7zftVoNO3bsSMek2LFjRxpAsDgwD3kNRSiQCMnNoX15REM/vcaevs5G97ovr+6FEjPJwNzcXDpGxv79+9NGZjP3bNIByWZmZrBjx47Uxyq5svdel+sztD6GyxkY8vl4kX1RYBb6jftmkFutVtHr9VAul1Pfsd6D2E0MGeAN1oaSBT+Ue/i7Ol4r9elD0jyw9zvXseuqHGxlYY/983x0G8si2WDoueu1cIhb3SdfDBISnjuLTbiM17eZyYDeD63mtigyNs8pc//6P0nYSqQ961jyroXPtV6vZ55fxPpAgwLtX661RdqgFkWHIZsax8Y8JSFPefK2846tvs72bLI1DwT9DpVJvovs8QRg0xcV0lfyT8k4oc+zyOZ1XRsMWIVhpakBPYb3v20H+ecRvbXGhpMBdfyzs7PpZBEzMzMjKQGuq1E/kC344z6B8GiC9iXhQ2cEr0ODWoP15Dmbw7bdkpaWljIPVuVwrk/D1H1q45gkSaZwst/vY25uLu2C1Ol00tHMNhspKJUODiPN8QS0Atg+Wy3usfl/TZvw2TFaDEnDnsOl89R76VX1WmKpEYQy/VarBQBp10PN+UasHuobZmZm0vECduzYkY5CqgrTSuEpgeqQbVGqpyKFSEDRpxd0KEFQnxMiCfxdU6UkBLSHwWCA+fl5zM3Npb5vM72XrNHhGCRsM+wYJAq9zzb4A5CptbDPJJTKGSf1EHqu3nsGIC1IBg7WEtBvrFe6YMPJAFWAWq2GPXv24BnPeEbK7rVgxkr/lGNDDFo/7TIOD2mJAHNoOh6AZ2AAMmyb8JwOI3kde0DrIjy2Z6+VTs/WMRw4cAD1eh29Xg+lUilTKbyZQDKwY8eOzNChNtpRY1VSx3eAY3xr9ytKh7aAyN4nZfk87jgDvGgtij4fvj8sVgOQVgzzOW32iGyjoTPM7d69G3v27MmMFwBkG/G8hsFbHkoL6DJP9rfvUij61+8h1dHC2oT6J2+MDcVwOES9XkeSJOl9IzHQgY02Q8pA1VXOVcKh6NXHcl0PSug8FUCPo20F778NDPV/793h/0XLGMToSKtzc3Op31uv8Us2lAzwhrKR9Lr8eA9BHwRh2Z4eAxitPA/l2vQ3NTzPuDVqtQzdHksnzgntV6/DM3Y93yRJ0nsGIFNQuZnUATVqK4mtVM7z1B2N8nm8POk2lAJQ8hg6F13Pvq8q/+UVsEWMB74ztAH2EtAxKVbbg8MSAXX2ViYmQqlHfobePysP230V3QPP13jfef4MsLRmSX3tamXvjQDtS7t0h1KLRSkC+8w9krdaqE8YZ11C2wle20pTmyvBhpABzf3Pzs5i165daQRnawMYWWvO3Rb6sQhGlwHZLkbKFrVhJuyoYHYfFl6DoudmIwdVEayiwG35m8IOTKTn1Gg0MDMzk+lZwIl0FhcXM+cxidCCUHYZtdOH2vtvG1g+/+Hw4NCtc3NzGcbPyUG8oYs9ebUoUiwybP3NvkPNZjO9BioYESuD1tLMzs6mw8xqWtGmbwjraPm/Pq+8Z6vPS+3RRuohkpkHGxnmSczeMbzgwjumBhIkUO12O50oKUmSTVHkqqNIcs4SHX4eyEb+Wi9CqD3bcWC8gE+3U1jVkdG7Kg1eDZqeZ0iFoko+NTWFUqmUDnPvpbAPFRtGBvjgZmdnceSRR2YqQjUVQMO2lZ6a02fjx4pZFmvRICgdas7eGo03Dvk48FQIXW6jV83JcX09H5uD9qRsrktjYC5pcXExrRzeDF0OVeZttVqZ7l9e7tMjCFQDOKa7bWSZRqGC4u2naOQ5Lgs1FpYkhO47h8nWXhARKwPTa9VqFdPT09izZ09mEisi9AyBbMSuz0sbAw/6fC2xJJRYhkiBt509R0tUrD/x9h9SJr1jE8PhMJ2eu9frYXFxMSUMk+w72DuEajJ9oU3b8Zo9P609sbQLpip7lOxtQKIIBYueT9D3Rz/1mPY49JNUb+jz1zolvKFkQIvqihpjkgI6epWE9UFq3lb3xyGBraF6Mp3+Ttibrizdg315tKrXIk++CjkO26CpBGjJzqQaNd8DEsO8oh/Ad2aWZHnO1DM07s9+FkWHFt699SRJVaVsYeikPp9JhNYTaTSoqpwqb0UNriqBobx93r7sOqEUl/6fZ5t2mdYm8X3henm2Mg601oDqq3bnnWTlirbkVdsDo35Z03Y2wOKn2r4WA3ObQyHw1s49IqDnacG0l/Uda4nDSgb4gJrNZjo0KAcF8YxZIzcuZ9/6Xq+HTqeTNgR2HHh9GbycmP3U38aJ9vWBqWrhrc9ug9zO2z4v5+jdEwvO1a0MXxWCSQKNku9BvV5Hu93OdLUEiqVVIEsKvYgdyFb36zFsHQffHx3dzXsuRdemBs13Yjg8OAAR999sNtNjbAZZdqNBtWhmZgZ79uxBvV5PexAokQSW1UN1suob7LDgOnWsbQCZsuR3rzBN17FFxJ5jL3qPbFGb+sUiUjsuOdD6B76L9KmLi4up0jhphIC2a3sf6Zgznp/0CL4lgXb4d1UZ1F946oHuk9DG3ioF40ADTqZOm80mpqamUC6X0ev10h5ka4ENIQP1eh2zs7NoNptpnlhZW0j+ApanCWZ+OCTt2QaFDpe1AeMajTYUShb0gapz8AobPcJhkadQeBKhvoSMsDmtLyMnnfNgUqCGSoPWcSQswwfyZxNUZSBECPi7vhO23zmh6lIov+edgz1ffU5AtpFgaoREbZLVm0mAFl5OTU1hx44d6aiCfG9sVKj3VAmBHdtfVSUvhUdyZwc681Qf2q6SB4txlERtROx3WyAXClzGgdYPsGFTW9ReVZMAq4Ly+bPnkUcE1G5tescjAnwPWHwMLL9/nv+2ZE2PRayGCOi2wHJAu7S0lPYICQ2ctlocNjKgdQJa+WvZm/dC80bqFMFFJMD7XiStreTGeg28t99xJEYLy0ptbsjmvRnxMk2gMtKhyIjrBY2wPKkv7xnZa6JzUGlYfwOyRm9ZOvfpNfLcR57DDaUJ9NOeH50LCVtEPhgZsbhUUy0hePKrNvp2lFGuw/VtdB+y35C/CgU2eeeb93/IN+UdxwYPIeWMv/MzRMo3GrR1tZ9xx+/3FNsi8H1YL7Ke5ztC0Huw1nVHh40McIhhDhE6OzubMlAgHGUlSZIaK+d6pozlFfPoy28bHCsn5iEvYrNqQYiA6PXYSLGIIebVFqiKorIZo80DBw6kwxdzLoNJgdaLcIpRFv/kqTZ5z4zbaSW5vg+aLiFh8iJ+bQzyCIH9Pe8d0UGyeFxWQvOYi4uLURnIAQcho6KoU5YD2dE9gSwRsD2O+MnvJAfcD5+/DnQGZNM9Nk2QFy3a30PP2RJTT272fE4e4cgj1JYQq1rGQsy1bmwOFeVyOX3u1ncAowXWCqsQaJDl2baSAKsqWqKkz8reWxuAcB391O+WtHkKFcfJ4fgya4XDRgb4ADnZEBsvvVGW5apERoNmxXxePpcPSx2xfYihYr4iWCMch8lxfb0mRV5U6i3nb3p8LcCkAuNFyxsNdTo6vkSI6YYckhIjRgqha7VyIM/DGrF+hs7dk//sOpacekqG17MlwgcbqFarlSGOeY2dpnqoBPCP3XCtYmRVGh0V1HvWoWPz3dLaBQ0g7Pr2cxy/tJpI1x7He3dJqCfxvaTt0HdoAelK9uGlARVaM6D1Xha2DclTGC3seqHCQT1vbddWEtiOi3UnAzxh9pVstVpoNpuuHMwLJrQBVSdK4wrlw9UQtbGwRYoeQlJz3ncPXqNhWX9elJC3f+9FVpJDWXUlMuXhgjbglih50OekOX4ra9pr9di73jOVAHkOlsFbePc7z7nzHfWKTb1zjMiCz5tdBzmy4EqmdvUavBDx1Mba5v2L1KqQTD/u/5YEeEQ+FDTlnYNCGy9bHKc+kjnpSVMGtCG0KUX9X9fXT13OfQGjRX9emkQVAiUT+rv3XffrqY2qCIxz/euZxllXMsAXiwMKHXHEEZienk6X64MERqMoe7OoLnB4Xx2L38uH6c3TCSw8hCJ+JSR2XT2WPV/vGkLSlPfyhJQHvpRq2HYd9rvVF35SYB2P3l8larbRtnKtVUV0n8Dy+2Ibe80d87griazGQVEdAIeApTIQMYpyeXkqYvoODlPN9KKVuQlLtu3z1XdF3zVv2HN9jzzp32uE9N1dCXQ7zw9p/YNNr+q12eNqw2HVCr2WUulgtTr3T7saVxVZbzDFaEea9MgBECZHVvEBlrvukQTps1YSwC7qXiRvfT3h3eeV3FN9B1QZUV+4Fs9nXcmAMjkODqGjsPHTUwlCsgwHj0mSJB2esQjKpIrYlMfgVgOPIFgiECIB9rghKcuerxInEqZJjDzznoO9bpX2vYpeOmnr3DQ1xH2pHBeSY8dh6OOmmPIMdJx3cbtD/YYOLKP5eyDfjkLraGPOdehPtDHIizztvqzvGNePhPxBkS8IvV957yevgXZABYIkQYvyrCoyKYQgr9eAtz6QnT/AEjluX9SoWvUwpLyGyOA49u49c7u9vo9riXUjA3zBmBbg6HL6AMcxMK5H6V/nLtCRmCwpKJVKI5XqwHJkTWMYt9HVxsO+RLq93UeouCTvvuUhT5bW41nGHLq+ww0rw6la4lV+a6GoVRN0n+q0NbIBssVgmi5aaZGXPX+voclTeRQkNpOYm50EsNcRRxa0Nqzw3m++W8z78tnr+oQqA7aIVe3IBhUraZRCCL0vnp3Tx/GabEO2kmNapYCgv+Q953s6Cb1eVFW0A6sRtpG2aRF7r7hca4m8GgEbtI4bwHKfGqiRgIXe27z2kNe/HvVg60IGeEGU+Dg4CMmAJQHjRIkA0n7GGuWxwbAzzAHLA5VwX3wQHHOApAIIN+T2OHou2jBwH8Q43/Uax4k4vAjCO281GM2pTwIZAPyUgMpuOk0nny2jRJJCa0C2obc9SPjOeWSgyKCt6mAdjX56DY0lkbwWbVAilsH7MjU1henp6cxYJED2PqtDzXPGwHLDrjVF/F2DBC6zjb6+Nx4xCEGJuhJQr+4npBIo8bQzqnpqlpJi7/1WG9Tjc9rc4XCYSvHrNUveSqEBnq0Bsz7RBggARj5VadTgxHueoWdvz4/b5/l56780mNVn4gVyqniGyPFqsa5kgLK+Zfa6Tt4+8vYN+GxPnUKoL7J2G1HjC3Xns9GrRht6XnmNvjVyPd/VNgbWAVpDXw8p6VDhsWrvOfCTf+Pm1lVFsr1JvIbXEi6LPCn2UI1wEp/PpIDPLVRXsVK78aJBu9xb30aEXoQYOo7aI1WKPITep7zlRVXoXM82KnqeGiipgha6xo2CF0CGonN7zV7QFdqf3k/v3h/K/Qj5GQt9z7xgbz2w5mRA2Rtlvna7ndsVyFuukrpl0DQsbqeFgdqw5L0o3K8yXptfsueoziC0nr2GcX4LRZ7eOYSg23NGMjaKk2LMgM/uS6Xl4hwAmZETNdJXQ7WOFljugpYkSSa3bAuMQo1wkUJgI0WSSpX+Qmkb3V4jj0l6NpMAe4/YMOk4FHZ9C9uw2QbAkmhVbvKOkdfoFJET2xjb4l/1dx7BDxUL24DHpiXzfJQXWfK+a+S90kLI9YT6AsAvtPZ8a8jHsnfaYDDIqCJe2tmmJrx7oz5g3HuX5w/yghG1lbVQB9aFDLAxmpqaQrvdRrvdzuTs8rblhdk+wNZQ2KiEGk+7viezUYa2bNA+RI0S7MPlOfJ3fUltvkr3l/e7rlPkYOwLXiotS31F6sVGgMZMtUifuZIBNq7aU0CjFULvUblcTq9dyYOSAWtEwMpzrnZf40R0uk2lUsnMrBmxDEvY7PO3jWXITrQmQ0mbRajx1e9q41zuHdc2TNy3fb+0odHAwtaghBoSfW/ypHIvKCoKZEi8rDowCWCjadUi+xyKzjfv3tFveqlI3U7hqb66rvZe0v15aS/CS/HYFNZaY13TBIr1boSsAepylehChRfWiEPHGIftWUZuG/2QHD1OZGG3GWe9vPU3AiEmrwZpn+E4sHnccY69FpgUZ7kVYN+FtXhni96HvHeNdjlOamEcrCQVVYRxAqGQTyuVSq4fsym7SYOn0lhYsjbOdSgJtQV+oWOH1AhdFlKZJ/HerjkZ0Gi7Wq2i0+mk/UJbrdZIPsYyH5seAEYVA25r+9sDByck4vFZQKiRhmV1Xt2BLlfosS3pIEKNHM/dKhB5yDN2eyxl/L1eDwsLC+h2u2k1/qSB98SqF/ZeaTSdBz5vFpjqtNZW3vMiJQ9k9TwvQt871g6o4uSBDkZ7SljSE7H8/DmUdqfTSZWUvPvrNWJeTx5vO++7wntfrG3qu2C3tQplUSPs/e7VW1mf6O1LSUGooVfCwKGaeb/5jk5CAxaKzIFRX2ujdA3IPEVHf2PqIHQOXtGmDUbtcwkFoPb902vUZ2yvVcfEKApix8W6kAEa7eLiIhYWFjLj0IdqB3jzrPPVm+PlZe10pN1uF91uN61G5uBGNmfMfRcpGFqLoMQjZHB2X+r086JWr2G35ESNP3Q89pbgffAkqo2GVQC8PKcSwVAhKH/nfWGFvqaXvHtoCYGek+6Xn14jY52LEtm861biULT+dsZgMEin4q7Vaim5A/zCS3svrSO2jUQRMfBk4SJFIK+YT3uceP4iFEV6ioRtAOx5e9dofZFtMLlOiAxMCjzpPJSe1W24vMjmi1Qk+922Cbpvj3Dp/0pCPKIbaujXK7hblzSBOnQy/HK5jH6/n5lUxtsuxFxDbBrwx54vehD8tDc7xICLmLFnhKH9AMXMT2Wu0DXYF4SNjBpx3rlsFJSU5clxwDLhy1NIFHSeyt6LnLgHdZxFKCIXfI42epyE7lqTCkualNx5jW6oMdV95fkX9SWhGh79tMgLMLz9eMcu2iakSnjrh4KFcc/Lvr+TDn1+od+VFBaRghBZXI0vsfvWY3pBRx7W05+vCxlgY7S4uIh9+/ah2+2m89YnSZLpZw0sF/uwIQsxNsCPKjkTWZIkGVlco0plj3mReehFUWdjWZ8yT50BTZkfr0GLcnRsbLtv/T1UaMbj6T0YDofpzI69Xm/ilIFyOTukKAvDgGWD0Maf12TnF/ciJq1+5twM+rtFyODt87XreTlFpjL0mXBdfucId95U3BFZqMLDcScWFxczhYRcD8jP2WpUrL/p9gqNNlVJHNePeEqEKoyerO/9HyLMXlRq9+NF/npuXvSsRCjvvm40vLbAphr1k+vo70WBUp7Ka/3VoV6DfZ+0tixP0bF+6lCxbsoAJaf5+fl0qsXFxcW04lsNy04h6r2ogB8xqrRl5VsdVEgNahxWba/H+273q9eukYzmvkN5cH2wOiSq5wTsPWBqgI0/Z3acxHoBkhzbtVDTIHrdvIbhcIhGo5FJFxHq9JRoVavV3EjBnldeoxFyHDx3nrc9hpJCPleSgVD+OOIgVO1bWlpKhyIviowJ3v9Qam8l+6I9FnUJ1UJlhTp/dfYkviGFzAsErOqVFxV7kadGyfYeKPnxFJWNhudrSRwtbKPp+XHvutSO80gEz8dLYYfs2lMG9J5756+Br0cK1gqHZaKiRqORdiXToj57I3hD84r8iNCD5Z8divdQ5B1rOEXHti+aNl623/848pxuH3oxuC/ecyolRUWKGwEv4gk5JMJraENqju5v3PkrvOg/z9j0mKpEhQzbkkLdLmIU5fLyKJI6yZj33hChCC+vQjx0bI/Aj4NQmsELMPKUKbtdyE/kvaMh31f0v32fVVGYJHjP07t/RcHdShpW25hr8LCS++OpGxZW/dR3Ua99rYjBunYtrNfr2LlzZzqkKFMF6jT1opSR2kYir1HjMLX6gNgoWoPwmJ1+8rtnhDYqtQ/SSpJczsaI0rh37d4DtUVwrLvo9XojhKBcLqcDDdEhdbtdzM3NpbM7TkLjwwaaURY/NcLWal51TEA2/w74I8SRDOr2Xi2J938okvRgf7OqD/srq9KjxUJWOYhYvofVajUdjrjdbqPVaqWKokLfC0/RUdXGa9BDBEFtTiNxG1XruoSN3kINjr5nIVVUz5HXoO9MiKQULc8LanRdrXHZaOh9CA2mVuTn+Duv31vXI3T2XnAd+8559hx6V3g9dhsNMmxKdJx2Y7VYd2WAIxBywhFKfYQlAzQ2vWmhwS802vYeamj42ZXePI8Q6L5C0bz3UniRMH/T9W1jx+OqdKovs0ZN9Xo93eckjkBIQmDvhf6v44bbAkIblXuS6TgRuOcA81i+97y53F4Hr4EV8MrsQ/uPOAjeO45iShLN0SU9x6mNtCWQ/B6KlIsKBpVIhJxvkUIRani8d89ej7duqIEJvfNqF5ZI5KkW1jYnCeoXgbCMb6ENeciW846p+7EItQeeemm3Cx3HtpErVavGxbqQAc2v0aBtl0JP3vGWewU0XM/bXx5jts4/5Bzs/vQvFDXmGZt3zbovLTwMXbtt5DQ6tuegx9PtV0OE1hr6DPSeKuvWBlNJke5D74feh1Bj66UZ8v737lXofyWw9vqU+HBkN0rfeRHrdoX6jkajkZKBPBv1loXsMORf8vYbaoQ96LtpI/+QX7Dvva2JGec8lBx7yAtcVJlggavtkbTRfgPwpXJgdCbAkO9Wmwz5Tvu7FzDwXOz2eQFEkY17hNRr+9T/rzVJW5fhiOnw6vU6Wq0W2u2221fcMnnAH7o371jjrAeMFg/lRc3q2D0ioPI896efvA6N2m00rC+bVpWH6in0/KmusC4g1ADZmb0mIU0ALDsfFtLxulnsyXNOkuVBiayBa/RniYLFOEQgr/hKtwk5VI00lNzx2ExbMX3BotpQQ7ddwfeAA0jNzMy4k5zZ73mO2NsuVHtQFC17Tl/346WZQpG23ZemNPKKWO394m/j+kPPr/GPxdhLS0tuF+WNBG1deyDZexYiA14QZ+17HKKnx7Tbqf3b89bnY58vr4n70/SAPUe9B6oar8UzWreaAZUzrDFzHW+7tXrpQux7NTfNe5nsfkINbUjZ0H1ZB6KOyXNOvJfMR4ccoX5OKvTl1wgb8Mkit9HtvHXzakxC50GMc8+sQ8mLIEjObGFa3uRd2xXqO3RCqyJlQH2HdbZ2XetkPXsjPLINYMSZ83cNEjyfUURc7XWMu+64Eai9Nhv9qzIQIj8bAQ2O7He9XystFs5TgULbadBVtI09/7zf85bbc15rVQBYpxEIh8NhZjjYfr8/ogx4NzC0LBQJ2K6FlGLt1LXejdR9cX/2YfMYqgjosT0UNcK2ZwChjaGu6+3XkzW1gdR+2XwGk2DQQDYfbJULfck958bCSSVQWmUOLPfn1ufD/YRIXJIkI6Pb5T3HcSILqhxs8FWp0S6vXB5HJMwi777r/zbCtuTRi8Ct3en7FjqeRwK8/YWiUN2+6Nqsv8mDEhvrIzyiqtGrLXimUtfr9SZqBMJQ4xcqBAwRI1sYaBv0vOsMKYee+qC/W6hSqNeg56ztl0b91j9yP2sVRK+LMqAOttfrodfroVarZaYHVYeqD8TeUCsPakPKsQW63S4WFhZQLpfRarXQbDaDzIk32UINRR0zWbI9b3735Es+HBuB8Jw92d570DTYUMFIyKksLS1hYWEhY9ST0tBoCklnItQiUa8gsN/vo9PpjIydsLS0hGazmdkGWL53lhTqO8ZPO9StznhoHW2eM9fUBRt+2yUOWJ5qmakDjodg37Xtirzo3MKL2kMIqTeHCqsY6Ps5bmrOU5TyrskLKIiQGqkEQKcJV1/X6/XQ7XbR6/XSVIF3focT1jeqEhMaxpe/E2xMV5oq9a5b/XJeoaaF9VHAsrqry636oaSHv9tZXNeCEKzboENANoeyHgaoDjSUr/FeCG9f9k+NRvcTik4IK9158M5xXMkntJ49Z96bSWD2FtaoPXh5U6oewPI91GltQxGap7ZYB+7dIz1Hb1/cj/6u18fr0MGl9NrzUkgRB6HRnNdgeuvnoch+89Ytek72nRr3HOxy69jzzkN/U4Lg2Zbnj63P079JCSBWiyKVNoQ8hUj3bYsM8+ARAQ/j+AMbNK6V/1hXMqCRtWVwIeZkG1Z+WjmIM5ppA6HRXKj63Ob0rBF40WMRO/eWa5GJnkPRi6PnHcqP8U+lPH72+33Mz89jbm4uTZ9MkkHbqETTBJ4D40iKnHyp3+9ntlHyo8PUanqHKSSP8NlGXiV8m8Lg8bgvza3qu6DKgtbLeM9/rSS+rQJNMTJKpYpknZ/ed31+/I37I/TZ8X/dR8g+9dmGinF1O1u3slJisBbviHdMJQL6pyNicnKoSR0q22sfitbPWy/v/uv9ynuG6r9Cjb6tb/K2D+1Xr8OmENYyoFi3cQY0kmPNQFEEXMR41IGTDLBx4A2iM/cMnJ8aXWp/fR0WWdfX89PzzLseW1TE7zy2rhPap5IA/d2Ob58kSZoSIBnYt29fOozrJIH3gNNM893wHB7TKiQBvMZyuZwOXqXpABvx5E1a5aVOaGxWfrPnT6LHYw8Gg5HaACUEXF+PWaSMbFfYmpfFxUUASOc1Uai96LOyjX5IQbAEXd8fbxtrlzZ6DpE9Vbm8AMEez3tf8wiBJ4vbfYWuVf2IpnWLpow+3LD3RJU3IKvcWb9s7biI9Ok90mHDPeVHSSrHw+A+Qu+QPRcb3Nhz0/dHlUYlBCtNf3hY10GHgGzkFnr5FUXSh1dToMzMOgU9D9sw2wjRrq/HWw/HHZLziBALtuep16Spk0lk9nmOU9cpYuJseG3xoe7DOj77uyoDRVGc3dZzSJa42Xcx9NwilqE2GXqPvcZSESLq1i+MI8fa74cSgY1ri2utFtl92eCI39Umttq7qQQx797ae6PqgL6H+qlKcFHAazHOuqGgdK2xrmSAxX3z8/NIkgStVmtk6uJxqrcJNgDAwUgByDpmFmxxub7UoZfdM4zQtXiRvEo3HuHQ9QFkFBLvmm0DE3Jo9hoZTbHwZ5InwtF7pKkhRvi2oeUzZdFdtVpNiwZtFK77JwllLwTCSsU8Nrsmka1beZrb8hlyZkTL7jU14L1/g8EgVTt0rIhJI24bATpeFozyXs/MzGTWAfLn+eAz9mY4VNh3TdcNybmhxlqVvDwUnbN3fiEFIuRHQtfsNXJWfdMxBiYFeg/yasR0Xd3G3ttQA2uPw0nfdEwU67/412q1MkGK9QFeXVFe0KDLVI301J618B3rTgYWFxdT50jpSS9ec71qSCE2yxvMCY8IL++saoSmA7w6AO/lsMvUCSl43vqCaCNgIxX7Augx8+RJNlZ6zmxcbJcgfXEnDbZh1ndBGTbfCZ1ngOSAvVOsMqTGSkLE2gkeTz95PnaKZJX7FXZ7zovhqRTWcfBTnxd7xEzic9ookAxwymKdfdLWAHjQ+6/rhRppddq6jlXtiuwz73q8qHGcfYUac/09TwXJ25bvPe81yakdzGySYBvCPIXZNv5qk16gZreh7+C90V5HOqhYqVRKB8pqNpsj75u+X6pShmbU9ciAvV4vzXWoWFcywJet1+sFc36rhUZoRQYT2j7vt6IXLY9x552HJQQebPGjPV9vO1UIJonRWyghs7IbCZVVYNjIa8FMqCsikDU6rVvRdUJkQNf1nIbn0L0ag6J3O0RKIw6ChIAqlxeprsR/WLsjYdcAxOZm1+IaDsd2494H+87zOLy3Njc+Se+mJfkczjvPT4f2Mc4xNEWl4+Wo/yL43mhhtA0iihAipPbcVBlZ62e1rmRgMBiks+aVSqWUFGgUFZLV7ItrpZ9QY+o1yjwOP70oQ9cJRR/aMHvS4jgPqKjBsOqINoI8d20A+RL2ej3Mzc1lZjWcJGMmKJEDSBt/jeR0GbA8ARMbXM4EaAv0uB2vm9Ln0tIS5ubmsLi4OEIc+L/2diEZ4PgATFGE3j9l96EGRFUjfV62oZvE57VRGA6HmQLhhYWF9LnrwGJFjYGnxtlnZZUpINwgFymIanfqF/Q4Rc85L6CwviPUE0ePZeVpLue7p8XYvV5vosYlAZbvAc+VNq9jeHi25z0LhUeO6Dtom91uF91uN1UJbAqYx6Zf4/tpfRP/qIzb5XnXrgSA/klTwZtCGWCaoNfrodlspifPB2f7XwPhKkyu4zXI9oF7EZ3dv34C2UZY84y6L8sGCWWUlgzYKNI+/Dwp0pKKvJdIpb5JLBwkaFTMyVsHrOqAOm3Nx9v7oA08r13vBw3abqejFVrlQGsOPGLKdXWf3juhz8q+J5M47OukIEmWe8g0Go1MqkenJue6eT6Dn0WqnM5+uhon663vqXTWN9lldj8hP0J49UX6zul52PebBGBxcTH9f5J6EQBZv0AyUK1WM+dZFBh6Cqtdl3+2Z0Wv1wuqrgxUuB3XpRI+HA4zvds0oPMCQ69nAPdDUqCj++pvh4rD0psAQFqM0el00lkM9SUfR74hih68dinzDCu0L48wcLndj0cMQscL/a8RI+FFPJrDZmNKJmq73U1a4Y8Fn48WO1L90D7i9l5674e+W/zkvjXHp40676s6UL1fjD4AZGpcbPSh74bWOuhvoXO2KZ1IAkahES0JXaVSwdTUVDpqIxCepVL3s5JjetuEGnnb2GgUZ7dfyb7HXZYXUepxleCQUClZ5ns+yb5D762eMxFSBor2SWiaxCukzLNTfU+VODD1SP8Weq4MYmytmT4/e+08t7X0HYeFDJDlP/300+h2u5iZmUkNmjfJNoBWzgKyfSxDhEAl33EdhBdpa1QHhMcfV6JgVQSPRYaM2sp+uqxWq6UEiuMIsJfGvn370sprDsI0qQYNHGxsO51O2uBSVms0GpiamnKjNo/Vq9GoGsC/ubk5zM/Pp427ju+gyoAlg1SyOGseC1V1iGuPpNj7bt8JdTY2TRBVAR+05YWFBTz55JOYn5/H7OxsWqyl6SK9f16kPI56YBt1Lst7rp7T9vxBkS+y52rPzwYRtlCa120LcLUhYzHcYDDA/Pw89u/fn/oOSuGTpgoo+v1+Osx6kiSZQmLA71qu9zfkV0K+g8o2bVS3ITTI4L3V95L+w6txUt/BT+/90QCQ6WCmQdcypbPuZIAgu6eEojfAEgLANzpgtJLfc84ruTmWfNjl9n/rALxo0ToQItRI60s8HA5HCtIoizF/zZHZSADI8idttEEPmnvjdTAHrO+Czc3pc1LHThYOIJNLU/ZsIx59bnqPeVwaGKVpVrNr+kj3Zc8pr0HQ9EOsFRgPS0tL6HQ6GAwGqNfrqVSsqkxRcaGn7tnl1m9oA8//9Tfb6Oo2ur46fa8RB0Z7Qely22hYH+jBngf/VwmcDR3rBCY9kFDfQdvWxtXeX15zXjGo2iTvDQMCjfCBYkWaSl+tVkv3VXRs7xo9kuANDrXWgcRhIwPD4TBlMnNzc9i3bx8ajQbq9TqazSYAX+rRvIgaP+ExdI9tK0I3z2PnoQhPh6H1DNkyecIrUrR1FJzjXv8WFhZQKpWwf/9+HDhwAPPz8+lL6w33PMngs9SXWics0iIcYPT+aZ5eG3tNm1B90EbX7ssjnBodzc/Pp0SMJJb/A9l5EbRR8Z6HKhSaItlMz22jkCRJ+izn5+dx4MABLC4uYmpqClNTU5lCLd1mNccZN5L3iEDe+kSocQrtI1RzoO+dBhJcXwmnku+FhYVMhL2ZCljZOPN96HQ66VgfntoXImH8TdU6jnbJoHVxcRFJkmT8PDBKHu358Tl0Oh0Ay/Ut2qVeVWBNU9q2g8+E50Q1gH5fFYu1wGEjA0tLS9i/fz/K5XL6cjYaDezcuTN1/l7hhD4QIOuA7XpWZvHyvOMQAa5rz0ENh99Dc61bqVI/vdQDI08OqKN56m63m6ZY9u7diyeeeCJVBJQdTroxA1lZn0oR3wn2K2daxBq3FhNSJWH0PhgMsH//fuzfvz+tp9BeFTRoO0CUfW+07uCpp57C3NxcGo0ybdBut1Gr1TLvpTogLXYjKHczzdPpdEYkyAgfvHckkOxatmvXLuzatQu1Wg31ej19J7yI2yo3REhW9nyRfue7YhFSGcaJuMd9D9iAWXVLr0P9g6qH8/PzmeK4onz4JIGERq+3Wq2i1WqNyPMWavfac4j3YGFhAQsLC6nv4DDYOheAV9OkZJD3kV2U6/V66jtarVaaDrUztSqJ84LOfr+P/fv3Y3FxEd1uN00TbLqaAYIMB0DKwobDIdrtdibytwarN0crMe2+vUjb7s9zBgoaVyhasw2Gvlj2eJrHCykU3nmpbK3b9nq9tBFhBFwUlUwibAROKY1yfL/fz1w/4BeJacGNVxXNdEHRufCTz1OdI/fFyEGZvPce8nysg9VGzP5tFke80aCdMSKsVquYmppKl4caAm00vcZZJfdx7KlIOfBwKEQgL3hRH6O+Q/vFa6GgRr8aSGwWaDtBhY2yPEekZSNroT6d903JAH0Hv9MPAaMKIM/FIwX8434ZHPB/r75Bt9Nr1cCT5G09uyMfNjKgYHRERYDSa6vVSqNihW1o86RVZfZ6c/MaZG7n5fr0QamT58Pi+dsCFW3IdbnmovidDZ/2V+V94npPPvlkWicwySOEjQMatBY78Z6wdkD/yOjV+dHBU1Egu9e0ib1HvL/2Wak6oO8AzwlAKk03Go00MtX3l9vz+dn6DS7X3+07GlEM9kpioRefwfT0dGpLrEEB/B47inGjd1tzEPInGrBopDgOyfDs2UaM9jy95UyZ0b9QGWCjtFnSAh54ziQ3HGdAlQJvwB9Nz/JP69j0fmnvI312qijqvbbfS6VSxr6r1Wo6Eq/WSPE8NcJXX6R1AnNzcyNDza/189sQMqCzz7GIrFarYc+ePZlZ42yDqoVa4zBp3jCb1/e2tw2BJ9fod21stHGxvR302Hz5+MmKVb3m+fn5dIhbSkPM8fEFm+RxBIqg98waEiMVLbzxjI3fybpp0GTPIcdq75vH8hWMDuhIy+WDsyUyV8nhRyuVSiZSWVhYQKfTGTFwGrFea8TKoN09SaxqtVqalqFikFe4pchLBxQFEKHcv0aS4zxj28DocgY+GoioL2JjASDzfmk9gBKAzew7gOV7xWtVdZBE0CMDngqoCrVNuXJd2yYoIQDCA82pYkzVs1KppGkCFoTzPbZtDq+RbQ3bDf19rbEhZEBfaI3stKuVzRd7REBlWP2fsBKhfZCrOe88hOoEeB62IpR5HytxakEcq6hJCrYKPKOzMj3fE3WIdh0d33+cftLeM8x7J9RISfp04iOmNlT2Z4GPdRoakW1mh7zR0AaB91nH1Ge3QyXpnkrn7Tfvf26/ls8u733Uxh9AptG337XxUF9qSehWee+8CHqcyn0lRGqzK/Ed+mn9mIUSf6oGVD9DQZGSAZ6b9mpYr2e4IWSAoBHPz8+nETW/N5vNtA9ps9lM5ViyKW7PTyULZOa8kWRfnpw/Drwcth1JStfjOWmDBgDdbhf79+9PCdDCwkImTaCFKmxUtrKcTEPRgiA+Rzp2awSeo6SxjVOZz3eE3/VznG1ZkEWJkoVt6mRIUOy2kQisLbRBfPrpp9Hr9dKBiegz+On10vFgAwz7Xd8fu421UT2Oys2eAqlqozbqWuSnjYPWLGm9i+7PkuetBLUhnYSMKqtdzws8GJCtNBjQdcexZ/sc6O+1gNAjFrr8cBC5UjLmEVbagK7oJEqlzINkDrDZbKLZbGLnzp0Z4/Z6FNh90XhsH1FKSfZ6rMFbGUgNkwVqSZKk+9MHbB8+FYCFhQX8+Mc/Tht5KiLjnMdWBZ8XiZ9HCvQeecZxuO9RXkPhfV9LbMb3YT19B/dPv1GpVNBqtdJuy/QdTOnY6a6Lzs0SB0vo9H0kLDmwDbJdDmBEWdJRRvldR8SzBa8hbMb3ZaXwAjWPBPDT/uXBkkePDKyWaIV8x3qh6BgbqgwQnnExz8I8MB9wpVLJRICeNG/3parBuAjJgTY6VZlH2b8aq44eRSYfKuLZDsZrYVmwRu6es9voSOdwNPoR44PvCRUi1ncwpUMbpQ/RbmJeKkG/e0rSuOfET20wVMHS35QMaHGxXc533+aZtzOsL9blnq3qs1jJMVajChTtc5IwEWSA0EZhYWEhrcBcWFhI+2jOzMykRSIc0EEHqdE0QZ70A4yy/jzS4BXl2JxeyNCV2bOv+VbK3x0KeA9CffYPhXlHbB+o7+CAU1rFXa/X077e7IrGtCPTB5quU9+g719RQ2KlXZWkgXCOX2tfWB+kvoO+xZOTtzv0XuTVha2WxIfu9Va79xNHBniDOYJTqVTCgQMHUCqVMDU1hV6vh3q9jkajgVarNTLNbGioznHkwLz1yMxZmKP9U9VwtUrckgE16IgsNjraj9jcUN+hqcGFhQWUy+UMGWg2m5nvbER0NDtVDmyUb2FTVpomtKoFu8RRwaAf0cp29SPRLsbDepGj7eSrJ4oMeFAmzIjcRvb1ej1l3trH2EsnhCaN0OPpcdnQ65zfOsSndinTKl+VpDRC2E4vV0TERoMNqhaa2kJdphBYgKi/e8GBFyHaOiEl/nps9RfsAaEBg/oL71gREeuFiSggHPf4VAFsNaZ+1z77XrSvA/zYik4t5lGjpDNhzt8zfmA0QiA2stgtYmthM74/G+k7VPLnADVq9wwebLpRuycW9ULS9KAGBFYZsPl+223WKgrcd0TEWqDoXdo0ZGAcqEEDyDB77U3gEQct6FOZThv/cfqiRkSsJzZj47BZfAcJAGsJSAy0BwKJgSX76jtspb+SAVUHoy+JOJwo8h0TnyZYCTSdEKoQpnGSADCloIbKVICy9CjxR0RsbdAvaCEafUYorcjtbFrR+o7oRyImHVtKGQD8HgLeOnbdkKyvv0UjjthobMZ3cDP5Dn56DX8oVZDnO/h79CERG41tlSaIiNjq2IyNSfQdEREbjyLfMd5sHhERERERERFbFpEMREREREREbHNEMhAREREREbHNEclARERERETENkckAxEREREREdsckQxERERERERsc0QyEBERERERsc0RyUBERERERMQ2RyQDERERERER2xyRDERERESsE+LoixGbBZEMRERERKwDvMnSIiImFVtq1sKVIjSzoZ2wxBpzaPYxO7lRnJwkImJrgtMZA+EJjHSZ9QfWN8TZDCM2GtuWDJRKJVQqFVQqlXSGMhp1tVpN5zWv1+vpNMc01n6/j16vN0IK9H+d15yfERERmxeVSgXVajX1EfxOYqDBgyUI6ht0imP1EVyu0yBHvxFxuLAtyYBG/p5B1+t11Go1VCoVNJtN1Gq1jCH3+31UKpWMUQMYMXbg4JSm3rSmERERmwv0F+VyGdVqFfV6PQ0q1I+ElAIAGQLQ7/fR7/dHvkdVMWIjsOXIgBojo3vb2PNTDVp/IxGgMlCtVtMGHzioDNTr9RH2znWGwyEWFxextLSE4XCYqgSDwSDD+nWfERERGwuN6D0/Uq1W0Ww20++1Wm1EYeS2uj9guVGnAsA/SwzoL0gM6EM0qIiKQcR6YEuRgVKphFqthlqthnK5jFarlYnwKf/zdxo0iYIlC8AyufAa/FDNwHA4RLfbTUlAp9NBv99Ht9vFvn37UmNfWlqKRh0RMSFgo65BQLVaxdTUVBo4TE1NZciB+g0Ame/6SYLAxh5AGhBQbVQC0Ov1MBwO0el0sLS0hKWlJXQ6nZRMkERE/xGxVthSZAA4aIyU7RqNBhqNBqrVKlqtVmrcjUZjhAxQKQCWGb01tnGkO5KEer2OXq+HwWCASqWCXq+HUqmEhYWF1AFEREQcXiixt8vpO6gO8o9BBb+TNDA1AIRJgO67XC6n6qANLjQ4WFxcRL1eT5UC7lvrlKziEBFxqNgSZICGWqlUMDMzkzb8zWYT9Xo9UwhIQ6e8Z4t8vO8WeV2FKpUKkiRBrVYDcNDYWYfAlESv10On08H+/ftT59Dv99fgTkREROTB2rUqiY1GI/UTVBLpR/id/oTEwfoCWy/g/U8fkSQJyuVyek4kKiQNg8EA5XI5LViu1Wqpqri4uJiqDCQJERGHgk1PBkqlEprNJqanp1Gv13HkkUfiiCOOGKkZ0PXtd8uy1ThDXYaK+g6TgGhtQL/fx86dOzEYDPD0009niEFUCyIiDh8YrU9NTWF6ejpVD9nws3BY1QItOAaQSv9WQfTIgKYgPVSr1ZFeScPhENPT0xgMBpk0QafTwfz8PPr9Pubn50dqCiIiVoMtQQYqlQrq9ToajQaazSZarRaArHS3moI9JQWhY+edlwWdCnOBJAtLS0uZ1ERERMT6QWV7RvtMH1oyAGSLkr10gCUCRX7BIwRUEBVUCZgqYBfEwWCQph1Zt8D1o/+IWC02LRnQAp7p6Wns2rUrLfDh7yF5jlBy4BEHa7QhQ/MGL/JSDjReMv49e/ag3+9j//79qFarqfy3uLiYe7yIiIiVQwuIm80mKpUKpqenMTMzkyEAXIfji3jjBeQ1+F4Q4fkTLg9BCUilUkGj0UhJA/0F654GgwG63W6aPtC6hIiIcbBpyQDVgGq1itnZWRx55JGZnJ6Xy9NPfvdSAlrcQ1lQl6lR63Yes7fnwCLFcrmMZrOJwWCQdlfq9/vYu3dvWkgUjTkiYm1Ae2M6YHZ2FtVqFdPT05ienk4LAr06gHFqiYrShsByw689C4rAAIbBD4uTW60WkiRBq9VKUwb79u3D3NxcpgdTRMS42LRkQPv3ej0DQtsAxfL/uGCEYD/zzlnPHUDK+BuNBgCkEYmtNo6IiFg92NjTT/A7/9SXKNS27XJgPB9i/UJIJcgDFYJQASQDDS2OjmQgYiWYWDJg83TMnbFIptlsYnZ2FvV6HdPT0xlZb9xcvzVSNTQb/fNTK4GB5VzfSogFj8sopNVqYdeuXansx25F8/Pz6Ha7I6MaRkREhEHfQfWQhJtqAOuKVC2wNhxSA0KqgfoULygoKkr29pX3O31do9FAqVRKCUaz2Uz9CLsxc8yC6D8i8jDRZECLfCqVSlpVmyRJaty1Wi0dCMSSgZUUAOZVA/NTmbnWFXjGH+rBoMu4T+YvOSZBo9HI5P5iT4OIiPFBn8E0AGuJdu7cmUbSTCd6PY4Uau9eKtGuq3+h6J+Nt7etVRg9RULPo9FooFarYTgcolKpoNVqpY1/qVRKBz6L/iOiCBNFBlQFoJTH7+x7y8icRtBoNEbkvfWsylcj1WNaMhGKDjxVgk4JQCr1AQcNnQMXsZI41hJERIShcjkbfc41oimB0IyDh2JbtgZpNShKS9jjAcsqJmsKhsNhet12eVQIIkKYGDJQKpUwNTWVynazs7Not9solUopi6fklSRJ2j+YUqA16lBxILAyg88brYy/226LNtKwkqP9TUnQ9PQ0Go0GlpaW0Gg00grhp556Ki0KIvOPiIg4CM3/z87OpkMIt9vtkdEDAbg9BYDiAsG8RhmAm9e3+/dsV0l+iEjodvRvqihob4nhcIhms5kOVrS4uJiOaRKDiggPE0UGWCXbaDSwZ88e7NixIx28R8kAgMwcAx4RWAlCkp+N4r39esadVyCkJMXLATabTTSbzdSYWTuwuLiIJEnQ6/XSsc0jIiKW1QCqhdpdUOcnob+wDS/tWqPmlfoQz/+EGtwQkbd+we7X652kYO0D9z81NZU2/iQKTLNGhSDCYsPJgPajrdfraDab6XwCdhIQLeCzvym8SHwc2c2el/c9dP55ysA4x/WW69DG7IZYLpfTIUk5YUlExHYF/QAHHWNqgGmBUEqgqLtgXhEg9xX6zVtWVJAYqg2wy1kbEDomofVWjUYjJSZUUeNEaREWG04GmPev1WrYvXs39uzZk1mmhXZqCKHI22PXKzFiHi+PDNhtaHhFjD5PIvS2037PVErm5+dRrVbR6/XQ7XbTiuGIiO0GNvzVahU7duxIewswTaA9dkLDkls/4fmNUMAxTkFhaN95xygi+KHARgkEgymSoX6/j4WFhfT7/Pw8Dhw4kKYMIiI2nAxQEajVami325iZmRmZH5ywBCDU4BJeI2lHGrTQiGAcqbDIweh+9XzUOej6nhLC//v9PkqlErrdblpQudpCpYiIzQ4tNJ6amkrrA1h7ZDFOj4GimgAvvTCOyhAKBPRaVAnI67Js05BWleT5aTChvQsWFxcz8xqEzjtie2FDyIAWzE1NTaX5Pa9nAFEUURetC/hjB4SQl1bwGvEiWCJQdFxLMij56aRMSZKg0+mkxh4VgojtADaSrDFiGk39h22ovR4+eXYY8g9FAchK6wQUeQrmuPDqlaz/4IBErElaWlqKdUgRG0MG2Je+Uqlg165deOYzn5kyeg7XO44xUE4bJ7enhYZe5O819jRgryEvigZWUtBoc4MeIajVakiSJJ1eld2H2Nug1+ulxZUREVsVOifJzMxMmlacnp5Oewt4QwoTeVH6ShRB7oufKu0X1QWFon3vXACM1CR569vftLdEpVJJC5J14iNOgTw/Px97GERsDBlgLotqQLvdzgylaWGNiC8yX14bCYR6AhQ10HmEwMr6RTnCQ+3hoOD1kuFzHAJ2w+z3+yse3jQiYjNCC46pCLB4sKiL8TiK3ripwbziwKIGtSh4UNCuPUIwLrS3Eic74n1j2tErVvT2E8nC1sWGkAHt/0vZSqfitC++fQn5m5dT85Z5DWWRxJfH1L3/verj0Hqhc/F+z+uGxPtINWVpaSl2GYrY0mB9AD91psFQjj2UIrDLigIEu636Cl1WVCjonWfIf6wGocBAVQH6DqYIOOIpRyz0EP3K1sZhJwOlUgmNRgM7duzI5L9trk+7z2h0Pk60rUU0HkKRQhHzHkdGHKceoOg3Oq8iB9JoNLBr167UkDudTnod0XAjthK0ToA+Y3p6Og0q1Hd4xXUeESDypir3bJHreCQgL6DgsUL+yUsjqKQfOkc9Vx0MzQP3xaneWWvE7spcFv3H9sNhJQNqrOwSNE4/4BDypgIN1QWEDHCtiu/GLVBaKTHwCAilUmB5wBH+RWOO2EpQ36EzENKH8Dddf7U2kLdtkRoQ8ie6XBtsm86038dBaP1QjYIO+c4BiThkcVFvq4iti8NGBshaS6VS2j+YEl9ofX3Jxym60eVF4xDocYBwTi7E9PW8QmpFyKi8/VnDtY7BM8xKpYKpqSnU63W0221MT0+j3++j0+nEYsKILQPmvMvlMhqNRjpkNwviAH+gr7weAaEJxnSdPIUAgEsGLEIR/LgpiZB/WglxCKVYddTTmZmZdJhzTmzU7/cjIdhGOKxkgAbNLkFTU1PBokFuA4ymDMYpAOJLX5Qy0OUc8U+PoVW2XG638eTJohRF3uiBRcWA3DfHZxgOh+h2u+h2u2nOL5KBiK2CUmm5zzxTi0oGvHkGaGchkq8KWiiC1oAkVFDsqQJ6DtZfjBukcDur9HmpUq+xD41DoNvpPQWQpgp0psM4wun2wWFPE/DPDifsSWdrgTyVgMfjZ8jQbX6Q16Lbq1GOy/rt70UpDQu9Lk/qi6w+YqtAfYbWFxWRfSC/IPlQ7MSqBOMgbzjhkP9YzfkV+T0Fu2Py3IpUk4iticNOBmjM/LPTAHs58nHlv3GOz+OwgWcjb1lwnqHbaEAJjZfW8Ji5VTi4T3VcqjqE0hJcj/O1c5jiTqcTpyyN2BKgmqjDlLPmKCS5E/Qnalfj5OdD6QC7zK7rSfkW3vgoNiWhy/NqokJ+qlQqZbazhEjPn/UDpdLBmWPZw6Df76dFhRFbH4e9ZkDZPclAESGwMptKYR6LLpIFCZ0SeTAYjLz4tnH39mEjjJBjUcOzy3Q/OgypR4qsksL1OBBRr9fDvn37UiKRl8+MiNgMIBkgIdBJiNQmQnK9ldlDv3kI2U+olsjCO7eVDCNugw1dHjqGd3xu79Up6bTOzWYTS0tLqFQqWFhYGOscI7YGNkQZ0NEAPYwrkY9jUEVjDKhCoCTD6+HgOR4uD527rTVgYx9yTt652uu00ijvK0cpZOTEvF8cpjhiM8Mqip7vWAupP+/4HolQ1YEI2dqhSO7jKqZF2+Sdm060pmmYmG7cPjhsZEClPg6SM25eyja8WjCk0S9Zt5XZbVTP35eWltDtdkcaTDoeJS88Hs/HKyZUaI0Bq3ItS/cIh16vzft5KgGXcayGWq2GnTt3IkkS9Ho9PPXUU3FmsohNDfYiYMGxBgJ5srxVAccl4LovL91nuwfqp2evNqWg55w3gJr+rsfTokk9tvUhXvrC+lLdtlQ6OI4Dp0fnQHAhtTVia+Gw9ybgC2aNzJP/PWPivrie/s5GnTKcRwYIEgcdccumMQBkRkbU89VPD0oYtB7BOpGQE1AlwZt4RLclaWER4czMDJLk4CRGc3NzWFhYiAw/YtOCqpc35LAl8UQoGraNd0jts/vRdXQkv7z0g3dcVQbVP2h9kOfrPD9QNESxqgMeYbD/0/fRj4yrvkZsDRwWMsCGudFouAZthyGmwYSgDSX/12PlpSC86NqSDz2vvPMIkYS89b2GfJzzDMG7HjpODijCLpOxm1DEZkSpVMoMUBayS69bXV5PAmIlJFl9g/qpokZTUwr2fGwhtbevvJ4Bq2mwQ2kPr54rqgLbA+tOBvhCNZtN7Ny5E+12G61WK5W1Qyzfa+B1uc3x6ye3yVMGuD4HPVLmbHOTll1zPQsdp4D7pMOw1xpyaN46nqphSYDKlO12O528Ze/evVhYWEhTBdGwIzYL+I5zQrOpqSk0Go2MXfC7R3RtA7zaKFcbfKoBoTx+3jgCSsh1e3scr2jR+gVeN32OrrNS4q/7Vn/IlO5gMIipxm2AdSUDaqycWKTVao0YdIjl50XmNu9mowEevyiyZ998NpShAseQ4Yb2q8aYpzgURf32HoWcjTom1mboUKNFx4qImERoCkwnJNLfx4FGuKtNmeWpeCGS76URtPEOpQQUVuK3QYq3fNzJhizR4b3W8Rw84hKx9bDuZICNEVmml4PPQ6hIL0++sr0FrKrAbVnDYNdV2c6SDM+o7TWreqGfgF/UyO300yM0oQFWrMMhk1c1IBpyxGaC2qHOQRDyHZ4/sHZol+fl+VeamguRe0sM2MjaAc5C29jj2HVC5xy6H9YXWl+p20dsL6wrGWB6oNFooNVqpd/tDIUhaMTvsWhvZkMW6zE6DxEJbl+v10f2oevZ81H2btMHaqC6P7J0rQAmE7ffdT+eHJpHBPjX6/XSnhKsDI6EIGIzgb2PqtVqOoeJN7upNnC0oXFSYV4jDPiNrPe7PVe1bT2G7S2g56tdmvPSkOoDAIyoI/b8eAx7P9SH6nf1HfYeFKmrEVsHh0UZ0EFCrDGv5EUbR6qyL7Yu96LwkKTnHUeNyxq2d7xQoVIRqw/95t0rz6A5gBK7WYauJyJiEkG7VH9RFECE7NDDWtiDjcJD5+U1yJ5aoGQ9pAzo/kLrFZ2D50Pz1IlIBLYP1oUMaJ6PE4o0m81VdVfRaFxz+0D2Jedv7Cpo5XU1ovVoGO01abRAA9R6BK/Gwe4rTwngd3tvOElRp9PJTFwUlYGIzQK+p+yBVK/XU3saJz8f2h+/h9IEXFYqlTJ+R+EtW0mvAj0HrxDYQ16aMM+m8wiSpwTofYkpxu2HdSEDZPQ6ta7OUGiNOMRW9Td9MdVggWUyMBwOsbS0hF6vlyEAWivgQaP8ohd/HAnSS2HoJCBePYJu69UUcF9eOkN/HwwG6HQ6OHDgQGYmw6gORGwmJEmSTnPOoYhpU149TRERsCQgr4G0NQD2OJ6fYGrSwq6rfswSm5Bdj1srpMezKQHuJ2+m1CRJ0rQisJxyjT2QtgfWpUpEDYgKgY7ot1LpyZtgI+8vlIsbJ3JYS9j7EMq/hVIEefcpJPXx+llAGJl9xGYEbUa7+a4F8mwhRByKtiOKbM2SEG2si9IMIZ8QCqLGRR4pCqVcI7Ym1lwZYFTMgh/KfOwW5OW81ChCRUAa/XusnC8tZ9oql8vo9XppRK6jfBUREusAvKrcPCehygCwzLptIZAnEdqUhl6/Pb6eBxv+fr+PXq+XFhF6AzNFREw6tDshZyckNE2o73WeYmbtJc+2bRoiZDuhlEHRYGShgMArQvSuZaW/67Xa67HKK9XEbreLxcXFdCj16D+2PtaFDFSr1cwsY5x2VPuthra1DFgJgEb8/M0u59SbxNLSUjqWAGsWdMztECu3cnyI1et3W1SkA5QU3TMlCnlV0SE1hNOOshfB4uJiSgaiIUdsJrBhZM0RB8KxKBqKVz/53SMBHmG24xJY+T0vmAjZLffFT/UxtP1QpB/yI+q/QraeR0b0fgwGA3S7XczNzaXp1jiF8fbBuvUmGEcezzOqovy9lxqw+Tjb/c9j7auBPa88x1CU0/RqA/KOqQ5EUyGaFtA0QSQCEZsJag+rTQ/kpdCA8Ein/D8kxR/quRRJ+utlq6F6BFVTVVXlX6wV2F5YFzKg0TrgM1FtoBWewfFlVuasjZ02gp7Re8TCNsAh9s9z9Br1kLJgr1GLdjRF4RVC6X2z+6TB8nqVADA10O/3ceDAAczPz6dGHRGxWaBjgFAd8AYq86R4r+G1AUPod+87cNA2WRxYNGqqYhwl0Z5/3u+6nk0z6rnaoml+1+6L9Els8JkWGAwG2L9/PxYWFtDv97G0tJR7jRFbC2tOBmykDvhFL7YngK7ndbWrVCojXQZJBnRgHa1+DclnoeGGPUJA4wuNYqbbe5KkVSX4acda0ON58qSSoMXFxdSYyepp0P1+H3Nzc5ifn0/vTUTEZoBNlenIgzYFN27Nj1UFdLm3rn5XX8CAYKUqnvfJ7zZI8tIVecqpPZ7XgyqkkrAL8mAwwPz8PObm5lLf0el0Ul8asX2wbsqARwpsdB+SofLk/NBLXiRpjSPlrxRq0N550ZmocqGEQAujbO6O2ysRUUZvyQCZvPYgiDJfxGbFuA1unipXhBChH6fBz4MNKELnpf5D/aQSE6/YOKR4htQPVU0ZOGlNAGuN4kBl2xvrQgYGg0HmBaPcpEyz6EVTQqBGoi+2hY3+gWwXJS/KGMfwrYphZUdb9MjjM6LodDro9XqZyKdWq6HZbI6QHt43XVevudfrpQqA5vY4yJAqB9GgIzYTbAOvNUf6uzZ43mBintRvofvwCpfz0n88bl7hsf0tlP7T3+kf2SBr7YQNMjz11Dtn7jdJkswQ5d1uN53NlL6DJCEqAtsT65ImsPnsfr+fkdq4HrCyxljJQF53Px3UR417JUQgJCMqMQAOGq7tEmQNc2lpCQsLCxkZj0RAZxSkwS4uLqbrkgxQAaBBs06APQY6nU5G3ouqQMRmhG308hrlcVIFeb9ZqV73mUcE7Lr8bv2P9Vuhc6Lix0aYg4RZIuD5TNtV2ztvNvD0Q/1+Px2YjMFDr9eLwcM2x7qmCdiIsd9/3ihdHmwOTSNzWyxjuxyGWLJNU4T69GtuzXZt1HMClhUPz5gWFxcxPz+PbrcLYNl42bgrkdD7BSDNmXI5mbsaN8lAHGAoYqvApsxCxYHjSvmhRk7JRtG+itaztQl5RcBeOoASPaV8W3Ss+wyNReL1vtD9qTLAFEH0GxHEupABvlwaEXNYUTs2v315bbGeNQ5l3zo8KffHBpL75nKdOdA7phbraY5fB+7Rc9Jr1evluWp1/759+1IywG3r9TqmpqaCVdHlcjntY00VgKRACwgZRajMGA07YrOD73O/389M+Qv43WxDtQJe7RKQVQ/HBf2GDjVuz8Neg/UntgGmzdJfJEmSDvaj++B3nrM3QJHX60KVVPpj3ldVA6LPiFhXZYBstNfrpb0BNDq3OXb9rnK8yt4aFehEQNqQexIaUxT22LqNMnDKdjQc7s9W8fJ3GjGj9oWFhfT/ubk5LC4uZoyuVqthaWkp6Ix4D7TroJIBVRGiIUdsJXiqnP4W+m5TDHl1RWrH2rDmRfaAPyphKFiwREQJgO0WTTLAQIIkn4qgNto6RLP1h0w76v3QmoFOp5PpfRURQazrFMa2fkCRJ8t5Ujxh6wC4jNtxgiRbEasV+nZ4U+/YeUU6yu5Z0KcGrcRA5T97b5g+ybtO7S1gazGivBex1aANqNbPhBqvvAbfIwj2uzf2PxtcSwxCCkDesXUfHpm3agCvm3avhcBKkiyBIRnQsQFselVJSPQbERbrTgYoZddqtZEX0Osuo9t60p6dxlR/o7zOxpLV9QAyVbL1ej1zHJ6LRwTUaagCsbCwgOFwiP3792Pfvn2pCkIjtnUH9tqLivxKpRK63a7bdTEac8RWhabcOp1O+q7PzMykJH6cvvT8tDbmFQx6pF/Ph/tizVOo1kiPz2Pzjw0xh/slEWDRryUA6vssKfHuAZcXFVXGACIihHUlA0C2S81KXsKQoXvGq795eUHup6ii14sc9H/+8ZqoBFB6W8mIf7FBj4jwQVulPZFMe43wOCkERV4j7jWkoV4F1l/kQYkBAySSAa3/0evM8w1aaxARsVZYVzJAxktpnsV9Gt0XjSamjT8nOvIMlERAGTPXY/rAyxfm9W6whTrqpLrdbmrMmjaIiIg4dFAdKJVKqNVqqUrGSYvG6RYMjHa9s7CV+eMSdI8MWDlfgyBVCamWsjYgNJx6RMThxLqTATaWlUolzf/VarWRIXkVVvrSwpii6t+Qk7BSm9ddxwMrdJkGYL/cubm5VBWIA3VERKwt1HeUy2UsLCwgSRI0Go00QNCeQXlKIj81zUco4c+LyG3hcF6BopX4tXaKNQMMJLrdbmz8IyYChz6FXwE0X6ZVrKECOM8wivr42oJCa7g8Dw+hPrvesW0hjjL6iIiItYNK6zr0ttctT7fxYMcTsOnAogDCWxaK4IsadltPEIlAxKRg3QsImQdbXFzE3r170e120Ww2MT09jUqlgkajgWazmTJtsm5bG5An7bPboG2omZaw8wAUwfZp9hwTZb6oCERErA+0EJhjdUxNTWEwGKBaraJWq6Fer4807nZ2Qf3T2fs8qB+yfyE1oAg8blFKNCJiI3HYCgi73S727t2LarWKdruNJEnSAXWYAgCWJbharRbM8QGj5ICEQKv6WeDHkfy4rrffULelUISiIwFGdh8Rsfag/XU6nZTQt9vttAap2Wym6zJlQP+gPQ+0MQaWpyUG/BlS9diqRHhBSlEhodYpFamQEREbiXUnAwQL7wCkUTWJQK/XG2Hzq4FnwFwOrG52wlCXvtjFLyLi8IAV+AAyPXaYdmTE7kXuXq+gokr9kK176Ue7P7udfnrrRERMCg4rGeDwvIzay+UyWq0W2u02qtUqZmZmUtVgJdCuOd1uNx0EiM7CDlu8kophHUI0GnBExMaAvkMLitnVUIfu9iR5b4RBOzU4P7X7n34ndGhk9SUkBV4PAu1FEP1IxKTisJEBdhUCDk7es7CwAABotVrodDppD4NWq5Wub+HJczQ0ziHAEQGB7MA+tlviuEV/WvgYERGxMWCqrtPppGmCcrmMer2ekoNqtZr6Be2OPI4iqKlA75P+SGuP8lKL+rsWC9r5Q1ZbhxARsdbYsASWJ8fpcg+hMcAVjByIQ8nR2W5BdozwKPdFRBxe2CJhG22HBgmysA0117WRvbdc9x1KAdBX6CeJgPUjERGTgMOmDITAxlsHC/IMjAhF6Kw14KAkg8EgHerT7ot5xlB3Iv6xtmFxcRHA8rTDOtpgVAwiIg4fdPyBRqORqo1JkmT8CG3cs09vHADatU4jrGlCTQ1oIaKN7O0ogrbwWP9iT6SIScJEkAHN8zEP6DHwov7AqgqMM04317PLrKyn04nquAnRmCMiDi+00WavITb6mgZU+/YGFlMS4I19YnsR0dZtrYIWFtJfaJ2A+hAlFnGMgYhJw0SQAe1J4OX1bb9/a/B2fzpC2bh9e61zsLUCrEdQIw+dQ0RExPrDq/r3RiQsyvEXwXYL9KYyD+3Lkoo47HDEpGJDyYBtuPnH4X+BbL7OzhyoeTytHKbhcqZEnYPcKgKeGsDphzm3OGU9Dj3c6XRiVXBExAZCJXxvimMqhJ6qaEmEVw/gDXoWGsXQFgbyXJhqYFEzxz3RqYQjIiYFE6cMqKGpgYXqCKwB81NziEXTjeoxVBVQgx0Oh2kNAY08IiJi42DVvNDvOg6BLuf3IuigZqHz8L4DyKQKVG2M/iNi0rChZIBGzNnJtPiGv3vbhAzZOocig7PVwjRUW6msYyNEMhARMRnwlIG8miJPbVxtZb8tavbqBKguMoig74hpgohJxIYrA5zFSxvjUqmU+a5DjXp5QmuM48wLbhUBNvasDdDvnKmw0+mk849HQ46I2FhoHY/1F4RttG39kQ0+rD8JwW6nBYj0QUw3Li4uYn5+PiUDcbrziEnEhpMBNuDlcjmNuFWuZwqhUqkEUwV22bjKgLedFvl4A5HYQUgiIiI2BrYRzovwvUbf+26x0kZbuxSy4beznEbfETGJ2HAywIh8MBhgbm4uHVlM/9idh+RA+xLzU4t2rBF6M5EBfjUy96cpDJ2hMBpzRMRkgKriYDDAwsICDhw4gGq1iqmpKTQajZH1bfrR2rHX8HsTo9kaBc//6MymURGI2AyYCDKgCkC320W1WkWr1UqHGq3Vaulwo+xxYNME/OOAJBrl9/v9dBhRG0GE/lgrwEGH1KAjIiI2HrTHSqWC/fv3p4OOjUPWuY7XM4DQ34bDYSb9oOtaMsAAotvtpqkCjokQawUiJhUbTgaAbAEfc/X1ej2ddpjKANUBb1tvsBD+jTMksVcRrOmKqAhEREwe1EY5GqHObEhYH8AIXecu0IZ63BlOrZoYSjVGEhAx6ZgIMgAsT2SUJEkaxZP1D4fDNH1A2R9AJl2ghYB2sCAaodfNUOsLbE8CZfmxaDAiYjIxHA7Tyck4WdHi4mI6oZEWIGv3Y27rDSZk5Xz1G+ovgGzw4PU+iumBiM2AiSEDAFIpjYZHeZ9KAYsMgXDDrqycoJrgDTxCKIP3yEBUBiIiJhcc1KdSqaRTmVcqFTQaDdTr9ZQY2EBCpztmKhJYbvDt+lzmkQFNE4R8R/QhEZOKiSIDQLaqnyoARwLUKUp1DgIr8YdYuCfV2UIgTVl49QURERGTB9otuyVTZaSf0HkEVCWg6giMBgv89OY+UNjGPpQeiP4jYpIxcWQAWE4ZsLBwcXExLRys1WrpPObtdnvEeK2cz99VUaBhewasRYPK8qMhR0RMNhhEdLtdLC0tpUOSM/rniKSVSiVNO1IN4Pd6vQ5gtLBQlQQey/YgYLqCw5XrKKYxTRAx6ZhoMkDQKKvVKhqNBsrlMtrtNprNZtqzQCU77Z3AZWq09liqRGiXIH7XkcsiIiImF0lycC4AwqYB2OOAacd6vZ6mERqNRqoEkCAASNVJhUb+6lvs/AMxvRixWTCRZMBC83Q63zjzcQotHLQsnnIfDZvfuT+rBsSiwYiIzQ3aPouP+cnaJBIFrUGydQKqEtjhj+lDVB2IPQgiNiM2BRkAsnOFKzGgvFer1TJGrXk+SofqEFhoRCOem5vD/v370wFMOERyZPYREZsX2t2YDTqHCFaVoFQqoV6vp2kF7YWgyiTX1ULlbreLbrebzmyqU51HRGwWbBoyAPhThVLqS5IkNWKCPREU5XIZvV4v7ZnAv4WFBczPz2fGFI+IiNg60Loi+hF+Z7TP7xzoDEAmuNBghD6IUxTr5ERRFYjYbCgejSciIiIiIiJiS6OURPoaERERERGxrRGVgYiIiIiIiG2OSAYiIiIiIiK2OSIZiIiIiIiI2OaIZCAiIiIiImKbI5KBiIiIiIiIbY5IBiIiIiIiIrY5IhmIiIiIiIjY5ohkICIiIiIiYpsjkoGIiIiIiIhtjv8HDK5ctCLhGrQAAAAASUVORK5CYII=",
                        "text/plain": [
                            "<Figure size 640x480 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
                "plt.style.use(\"default\")\n",
                "plotting_image_0 = np.concatenate([images[0, 0, :, :, 15].cpu(), np.flipud(images[0, 0, :, 24, :].cpu().T)], axis=1)\n",
                "plotting_image_1 = np.concatenate([np.flipud(images[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
                "image = np.concatenate([plotting_image_0, plotting_image_1], axis=0)\n",
                "\n",
                "ax[0].imshow(image, vmin=0, vmax=1, cmap=\"gray\")\n",
                "ax[0].axis(\"off\")\n",
                "ax[0].title.set_text(\"Inputted Image\")\n",
                "\n",
                "plotting_image_2 = np.concatenate(\n",
                "    [reconstruction[0, 0, :, :, 15].cpu(), np.flipud(reconstruction[0, 0, :, 24, :].cpu().T)], axis=1\n",
                ")\n",
                "plotting_image_3 = np.concatenate([np.flipud(reconstruction[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
                "reconstruction_3d = np.concatenate([plotting_image_2, plotting_image_3], axis=0)\n",
                "ax[1].imshow(reconstruction_3d, vmin=0, vmax=1, cmap=\"gray\")\n",
                "ax[1].axis(\"off\")\n",
                "ax[1].title.set_text(\"Reconstruction\")\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "54ef9b14",
            "metadata": {},
            "source": [
                "### Cleanup data directory\n",
                "\n",
                "Remove directory if a temporary was used."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "id": "d53b24f4",
            "metadata": {},
            "outputs": [],
            "source": [
                "if directory is None:\n",
                "    shutil.rmtree(root_dir)"
            ]
        }
    ],
    "metadata": {
        "jupytext": {
            "cell_metadata_filter": "-all",
            "formats": "auto:percent,ipynb",
            "main_language": "python",
            "notebook_metadata_filter": "-all"
        },
        "kernelspec": {
            "display_name": "Python 3 (ipykernel)",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.11.9"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}
